Search Results (36)

Ensuring the trustworthiness of artificial intelligence (AI) systems is critical as they become increasingly integrated into domains like healthcare, finance, and public administration. This paper explores frameworks and metrics for evaluating AI trustworthiness, focusing on key principles such as fairness, transparency, privacy, and security. This study is guided by two central questions: how can trust in AI systems be systematically measured across the AI lifecycle, and what are the trade-offs involved when optimizing for different trustworthiness dimensions? By examining frameworks such as the NIST AI Risk Management Framework (AI RMF), the AI Trust Framework and Maturity Model (AI-TMM), and ISO/IEC standards, this study bridges theoretical insights with practical applications. We identify major risks across the AI lifecycle stages and outline various metrics to address challenges in system reliability, bias mitigation, and model explainability. This study includes a comparative analysis of existing standards and their application across industries to illustrate their effectiveness. Real-world case studies, including applications in healthcare, financial services, and autonomous systems, demonstrate approaches to applying trust metrics. The findings reveal that achieving trustworthiness involves navigating trade-offs between competing metrics, such as fairness versus efficiency or privacy versus transparency, and emphasizes the importance of interdisciplinary collaboration for robust AI governance. Emerging trends suggest the need for adaptive frameworks for AI trustworthiness that evolve alongside advancements in AI technologies. This paper contributes to the field by proposing a comprehensive review of existing frameworks with guidelines for building resilient, ethical, and transparent AI systems, ensuring their alignment with regulatory requirements and societal expectations. Full article

In this paper, we present a general methodology suitable for analyzing any IBG (Integrated Bragg Grating) as a linear time-invariant (LTI) system using the effective refractive index (ERI) and transfer matrix method (TMM). This approach is based on the translation of the IBG’s physical structure into a matrix of effective refractive indexes, n_eff, which is wavelength-dependent and describes the behavior of light in the IBG while avoiding the use of approximations like Coupled Mode Theory does. This procedure allows to obtain very accurate reflection and transmission spectra, regardless of the perturbation complexity of the grating. Using this methodology, different apodization and chirp methods are revised and compared. Its generality is considered by analyzing two distinct technological platforms, silicon-on-insulator (SOI) and aluminum oxide (Al₂O₃). Full article

Coupled ship simulation in hydrodynamics and structural dynamics provides a comprehensive approach to understanding the dynamic behavior of ships under wave-induced loads. Improvements in computer power have made it much easier to create coupled simulation methods that combine structural and hydrodynamics analyses. A literature review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 is used to look at future trends in this literature review. We have filtered 1440 articles in PRISMA 2020, including 93 articles for analysis. The bibliographic analysis reveals that China emerged as the first according to the first authors due to significant industrial and funding support. Based on 93 articles, computational methods can be grouped by the coupling method (one-way and two-way), the hydrodynamic analysis approach (potential flow and CFD), the structural analysis approach (FEM, TMM, and DMB), the hydrodynamics element type (2D and 3D), and the structural element type (1D and 3D). As an outcome of the review, it can be concluded that the most common approach is a two-way connection of the potential flow and FEM methods, which both use 3D elements for structural and hydrodynamic analyses. Future trends of this research should be explored based on the application of variables, reducing computational resources, and using artificial intelligence. Full article

This study presents the optimization of two SPR biosensors, Sys₃ and Sys₅, for SARS-CoV-2 detection at concentrations of 0.01–100 nM. Sys₃, with a 55 nm silver layer, a 13 nm silicon nitride layer, and a 10 nm ssDNA layer, achieved a figure of merit (FoM) of 571.24 RIU⁻¹, a signal-to-noise ratio (SNR) of 0.12, and a detection accuracy (DA) of 48.93 × 10⁻². Sys₅, incorporating a 50 nm silver layer, a 10 nm silicon nitride layer, a 10 nm ssDNA layer, and a 1.6 nm tungsten disulfide layer (L = 2), demonstrated a higher sensitivity of 305.33 °/RIU and a lower limit of detection (LoD) of 1.65 × 10⁻⁵. Sys₃ outshined in precision with low attenuation (<1%), while Sys₅ provided enhanced sensitivity and lower detection limits, crucial for early-stage viral detection. These configurations align with the refractive index ranges of clinical SARS-CoV-2 samples, showcasing their diagnostic potential. Future work will focus on experimental validation and integration into point-of-care platforms. Full article

In a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase significantly. To improve users’ charging experience and ensure stable operation of the MMG, we propose a new joint scheduling strategy that considers both time cost of users and spatial load balancing among MMGs. The time cost encompasses many factors, such as traveling time, queue waiting time, and charging time. Meanwhile, spatial load balancing seeks to mitigate the impact of large-scale EV charging on MMG loads, promoting a more equitable distribution of power resources across the MMG system. Compared to the Shortest Distance Matching Strategy (SDMS) and the Time Minimum Matching Strategy (TMMS) methods, our approach improves the average peak-to-valley ratio by 9.5% and 10.2%, respectively. Similarly, compared to the Load Balancing Matching Strategy (LBMS) and the Improved Load Balancing Matching Strategy (ILBMS) methods, our approach reduces the average time cost by 31.8% and 25% while maintaining satisfactory spatial load balancing. These results demonstrate that the proposed method achieves good results in handling electric vehicle scheduling problems. Full article

This work presents an original and very interesting approach to a calculus problem involving beams with intermediate supports through the transfer-matrix method, a very easy method to program to quickly obtain good results. To exemplify the applicability of this approach in dentistry, the calculus of a dental bridge on three poles is explored. Dental restorations are very important for improving a person’s general state of health as a result of improving mastication and esthetic appearance. The approach used in this study consists of presenting a theoretical study about an indeterminate beam with an intermediate support and then particularizing it for application in a dental restoration case, with a dental bridge on three poles and two missing teeth between the three poles. The bridge is assimilated to a simple static indeterminate beam. This paper is unique in that it involves the application of the transfer-matrix method for a case study in dental restoration. The assimilation of a dental bridge with a statically undetermined beam, resting on the extremities and on an intermediate support, is an original approach. The results obtained in the presented case study were validated by comparison with those obtained through the classical calculation of the Resistance of Materials, with Clapeyron’s equation of three moments. Due to the ease and elegance of solving various problems with the TMM, this approach will continue to be relevant to other original case studies with different modeling requirements, and these applications will be presented in future research. Full article

Background/objectives: Eustachian tube dysfunction (ETD) presents complex diagnostic challenges in otolaryngology, compounded by concurrent chronic nasal disease. Patient-reported outcome measures (PROMs) often assess ETD severity due to its elusive diagnosis. Tubomanometry (TMM) emerges as a promising diagnostic tool, yet its application alongside chronic nasal disease remains unclear. Our study aims to elucidate TMM’s role in ETD diagnosis within the context of chronic nasal diseases, integrating subjective assessments, clinical examination, and TMM results. Methods: A prospective observational study was conducted with patients suffering from ETD and chronic nasal disease allocated in three different groups according to their nasal pathology. Clinical examination, PROMs in the form of ETDQ-7, and NOSE questionnaires as well as TMM were performed. Results of the above subjective and objective measurements were analysed and correlated statistically to determine the value of TMM in chronic nasal disease patients. Results: All recruited patients suffered from ETD and chronic nasal disease, with similarly affected ETDQ-7 scores across all groups, while NOSE scores differed significantly based on the underground nasal pathology. TMM values confirm the presence of ETD in all three groups, confirming the role of TMM within this cohort. Interestingly, TMM values can still confirm the presence of ETD in patients with chronic nasal disease but cannot discriminate among chronic nasal pathology patients, making TMM a diagnostic tool with uniformity among the chronic nasal pathologies. Conclusions: ETD in individuals with chronic nasal disease presents distinct complexities, requiring a tailored diagnostic approach. In this context, a thorough clinical assessment, integrating ETDQ-7 and NOSE questionnaires, supplemented by TMM where accessible, is crucial to confirm diagnosis. This study confirms that TMM can diagnose ETD in all nasal pathology patients without being influenced by the nature of the disease. This research endeavours to refine diagnostic strategies, enriching clinical decision-making, and enhancing ETD management in patients suffering with chronic nasal diseases. Full article

This work studies an integro-fractional differential equation (I-FrDE) with a generalized symmetric singular kernel. The scientific approach in this study was to transform the integro-differential equation (I-DE) into a mixed integral equation (MIE) with an Able kernel in fractional time and a generalized symmetric singular kernel in position. Additionally, the authors first set conditions on the singular kernels, whether related to time or position, and then transform the integral equation into an integral operator. Secondly, the solution is unique, which is proven by means of fixed-point theorems. In combination with the solution rules, the convergence of the solution is studied, and the error equation resulting from the solution is a stable error-integral influencer equation. Next, to solve this MIE, the authors apply a special technique to separate the variables and produce an integral equation in position with coefficients, in the form of an integral operator in time. As the most effective technique for resolving singular integral equations, the Toeplitz matrix method (TMM) is utilized to convert the integral equation into an algebraic system for the purpose of solving the position problem. The existence of a solution to the linear algebraic system in Banach space is then demonstrated. Lastly, certain applications where the functions of the generalized symmetric kernel are cubic or exponential and it assumes the logarithmic, Cauchy, or Carleman form are discussed. In each case, Maple 18 is also used to compute the error estimate. Full article

The traditional approach to optical design faces limitations as photonic devices grow increasingly complex, requiring advanced functionalities. Recently, machine learning algorithms have gained significant interest for extracting structural designs from customized wavelength spectra, surpassing traditional simulation methods known for their time-consuming nature and resource-demanding computational requirements. This study focuses on the inverse design of a reflectionless multilayer thin-film structure across a specific wavelength region, utilizing a tandem neural network (TNN) approach. The method effectively addresses the non-uniqueness problem in training inverse neural networks. Data generation via the transfer matrix method (TMM) involves simulating the optical behavior of a multilayer structure comprising alternating thin films of silicon dioxide (SiO₂) and silicon (Si). This innovative design considers both reflection and absorption properties to achieve near-zero reflection. We aimed to manipulate the structure’s reflectivity by implementing low-index and high-index layers along with Si absorption layers to attain specific optical properties. Our TNN demonstrated an MSE accuracy of less than 0.0005 and a maximum loss of 0.00781 for predicting the desired spectrum range, offering advanced capabilities for forecasting arbitrary spectra. This approach provides insights into designing multilayer thin-film structures with near-zero reflection and highlights the potential for controlling absorption materials to enhance optical performance. Full article

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems. Full article

Area selective deposition (ASD) is a promising IC fabrication technique to address misalignment issues arising in a top–down litho-etch patterning approach. ASD can enable resist tone inversion and bottom–up metallization, such as via prefill. It is achieved by promoting selective growth in the growth area (GA) while passivating the non-growth area (NGA). Nevertheless, preventing undesired particles and defect growth on the NGA is still a hurdle. This work shows the selectivity of Ru films by passivating the Si oxide NGA with self-assembled monolayers (SAMs) and small molecule inhibitors (SMIs). Ru films are deposited on the TiN GA using a metal-organic precursor tricarbonyl (trimethylenemethane) ruthenium (Ru TMM(CO)₃) and O₂ as a co-reactant by atomic layer deposition (ALD). This produces smooth Ru films (<0.1 nm RMS roughness) with a growth per cycle (GPC) of 1.6 Å/cycle. Minimizing the oxygen co-reactant dose is necessary to improve the ASD process selectivity due to the limited stability of the organic molecule and high reactivity of the ALD precursor, still allowing a Ru GPC of 0.95 Å/cycle. This work sheds light on Ru defect generation mechanisms on passivated areas from the detailed analysis of particle growth, coverage, and density as a function of ALD cycles. Finally, an optimized ASD of Ru is demonstrated on TiN/SiO₂ 3D patterned structures using dimethyl amino trimethyl silane (DMA-TMS) as SMI. Full article

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial–mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer. Full article

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log₂ fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log₂ fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies. Full article

For over two decades, vascular stents have been widely used to treat clogged vessels, serving as a scaffold to enlarge the narrowed lumen and recover the arterial flow area. High-purity oligocrystalline austenitic steel is usually applied for the production of stents. Despite the popularity and benefit of stenting, it still may cause serious clinical adverse issues, such as in-stent restenosis and stent fracture. Therefore, the study of the mechanical properties of stents and in particular the prediction of their life cycles are in the focus of materials research. In our contribution, within the finite element method, a two-scale model of crack initiation in the microstructure of stents is elaborated. The approach is developed on the basis of the physically based Tanaka–Mura model (TMM), considering the evolution of shear bands during the crack initiation phase. The model allows for the analysis of the microstructure with respect to the life cycles of real materials. The effects of different loading conditions, grain orientation, and thickness of the specimen on Wöhler curves were analysed. It was found that the microstructural features of oligocrystals are very sensitive to different loading conditions with respect to their fatigue behaviour and play a major role in fatigue crack initiation. Different grain-orientation distributions result in qualitative and quantitative differences in stress distribution and in the number of cycles for crack initiation. It was found that presence of a neutral zone in the cut-out of the microstructure under three-point-bending loading conditions changes the qualitative and quantitative patterns of stress distribution and affects the number of cycles for crack initiation. It was found that under both tensile and bending loading conditions, thicker specimens require more cycles for crack initiation. The Wöhler curves for crack initiation in oligocrystalline microstructures of stents could be compared with the ones in the experiment, taking into account that for high cyclic fatigue (HCF), typically, more than 70% of the cycles refer to crack initiation. The developed numerical tools could be used for the material design of stents. Full article

The Transfer-Matrix Method (TMM) is an original and relatively simple mathematical approach for the calculus of thin-walled cylindrical tubes presented in this work. Calculation with TMM is much less used than calculation with the Finite Elements Method (FEM), even though it is much easier to apply in different fields. That is why it was considered imperative to present this original study. The calculus is based on Dirac’s and Heaviside’s functions and operators and on matrix calculation. The state vectors, the transfer-matrix, and the vector corresponding to the external efforts were defined, which were then used in the calculations. A matrix relation can be written, which gives the state vector of the last section depending on the state vector of the first section, a relation in which the conditions of the two end supports can be set. As an application, a heat exchanger was studied, with a large cylinder subjected to a uniformly distributed internal load, and from the inner cylinder bundle, a cylinder subjected to both uniform internal and external loads was considered. For the second cylinder, two possibilities of action for the external forces were considered, a successive action and a simultaneous action, achieving the same results in both situations. The TMM is intended to be used for iterative calculus in optimization problems where rapid successive results are required. In the future, we want to expand this method to other applications, and we want to develop related programs. This is an original theoretical study and is a complement to the research in the field on thin-walled cylinder tubes and their applications in heat exchangers. Full article